Dimensionality and reliability of a screening instrument for students at-risk of dropping out from Higher Education

Casanova, Joana R.; Gomes, Cristiano Mauro Assis; Bernardo, Ana; Núñez, José Carlos; Almeida, Leandro S.

doi:10.1016/j.stueduc.2020.100957

Cited by 38 publications

(26 citation statements)

References 53 publications

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“…However, democratizing access does not equate democratizing academic success, as these students present several difficulties when they have to face HE challenges (Adabaş and Kaygin, 2016;Casanova et al, 2018;Tight, 2019). This study illustrates more problems in academic adaptation and higher academic failure and dropout rates in low sociocultural subgroups (Díaz-Mujica et al, 2019;Casanova et al, 2021b). The problem is accentuated when these students are also not in a program and/or in an institution of first vocational option, due to a numerus clausus system on access (Fonseca et al, 2014;Ferrão and Almeida, 2018).…”

Section: Discussionmentioning

confidence: 86%

“…His holistic approach points to teachers, academic staff, and peer relationships as relevant variables in the process of dropout decision-making. Research in this area evidences that student dropout can be related to personal characteristics (e.g., abilities, competencies, and motivations), family characteristics (e.g., first-generation students, sociocultural, and socioeconomic situation), academic background (e.g., previous achievement and vocational choices), and contextual characteristics (e.g., curriculum organization, size, and quality of institutions), among other ( Häfner et al, 2015 ; Lindblom-Ylänne et al, 2017 ; Casanova et al, 2021b ; Wangrow et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Promoting Success and Persistence in Pandemic Times: An Experience With First-Year Students

et al. 2022

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The transition and adaptation of students to higher education (HE) involve a wide range of challenges that justify some institutional practices promoting skills that enable students to increase their autonomy and to face the difficulties experienced. The requirements for this adaptation were particularly aggravated by the containment and sanitary conditions associated with coronavirus disease 2019 (COVID-19). With the aim of promoting academic success and preventing dropout in the first year, a support program was implemented for students enrolled in two courses in the area of education at a public university in northern Portugal during the first semester of 2020/2021. Three sessions of 50/60 min were implemented, namely, the first session focused on the verbalization of the demands, challenges, and difficulties of the transition, and the second and third sessions focused on the difficulties of academic adaptation and academic performance. Data from a dropout risk screening instrument and from the activities performed during sessions were analyzed. The main results point to student satisfaction with the content and the activities of the sessions and their usefulness. Students report not only high satisfaction levels with HE attendance, but also some emotional exhaustion due to academic activities. The continuity of the program is recommended with some improvements in its planning to ensure a more definitive version of the program in the next two years.

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Section: Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Promoting Success and Persistence in Pandemic Times: An Experience With First-Year Students

et al. 2022

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“…Measurement in question was when the instrument was used to measure the same conditions will produce results that are not much different. Reliability is the consistency of instruments that provides the same information when tested repeatedly (Casanova et al, 2021;Ko et al, 2017;Sa'idah et al, 2019). The formula used to perform reliability tests on instruments with dichotomy properties is the Kudor Richardson formula (KR-20).…”

Section: Coefficientmentioning

confidence: 99%

The Concept of Heat Transfer measured by Cognitive Domain Assessment Instruments

Yuniasih

Yudiana

Japa

2021

JISD

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The lack of availability in assessment instruments used and unmet cognitive levels of C3-C6 resulted in poor quality assessments. This study aims to develop cognitive domain assessment instruments on the concept of heat transfer material in grade V elementary school. This research was development research that used 4D models (Four-D models) to define, design, develop, and disseminate. The subject of study was the assessment instrument of the cognitive realm with the object of research, that was the instrument quality. Measurement of the validity of the content was done on two experts/judges using the non-test method and instruments of the expert validity sheet. Instrument quality measurement was conducted with field trials of 106 students using multiple-choice objective test instruments. The content validity data obtained were analyzed with the Gregory formula, while the field test result data analyzed the validity of the grain, reliability, different power, and difficulty level. The results showed that the validity of the content obtained by 0.97 (very high) with the test results of the item's validity got 30 points of the question was declared valid. The instrument reliability index was developed by 0.85 (very high). The average coefficient of different power obtained is 0.51 (good) with a test device difficulty of 0.52 (medium). The result demonstrated cognitive domain assessment instruments that were developed feasible and can be used in conducting learning assessments in the cognitive realm.

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“…For example, first year grades were found to be the best predictor of graduation at the University level (Willoughby et al, 2021). A host of psychological factors like academic exhaustion, satisfaction with education and dropout intention have also been explored as well (e.g., (Casanova et al, 2021)).…”

Section: Introductionmentioning

confidence: 99%

Exploring Predicting Performance of Engineering Students Using Deep Learning

2021

18th International Conference Cognition and Exploratory Learning in Digital Age 2021

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A significant amount of research has gone into predicting student performance and many studies have been conducted to predict why students drop out. A variety of data including digital footprints, socio-economic data, financial data, and psychological aspects have been used to predict student performance at the test, course, or program level. Fairly good prediction results have been achieved using both traditional machine learning and more recently deep learning methods. While using diverse sets of data has achieved good results, this data is often difficult and expensive to collect, and may have privacy-related issues. This paper explores the extent to which only prior performance data readily available with registrars in most Universities can be used to predict student performance in future terms. Twenty term data from 789 students enrolled an engineering program at an American University were used to train long term short term (LSTM), Bi-directional LSTM and Gated Reference Units (GRU) models to predict student performance in future terms. The results are that all three types of models were able to reasonably predict the next term's performance (F1-score of about 0.70) regardless of the number of terms a student had spent the University. The models generally did not overfit. The prediction was reasonable until about trying to predict performance on seventh term in the future, but the performance dropped beyond this point primarily due to lack of sufficient data (F1-score of about 0.2).

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Dimensionality and reliability of a screening instrument for students at-risk of dropping out from Higher Education

Cited by 38 publications

References 53 publications

Promoting Success and Persistence in Pandemic Times: An Experience With First-Year Students

Promoting Success and Persistence in Pandemic Times: An Experience With First-Year Students

The Concept of Heat Transfer measured by Cognitive Domain Assessment Instruments

Exploring Predicting Performance of Engineering Students Using Deep Learning

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